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Reconstruction of Accelerated DCE-MRI Guided by Image Quality Metrics1Biomedical Translational Imaging Centre, Nova Scotia Health Authority, Halifax, NS, Canada, 2Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada, 3Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada, 4Physics, Carleton University, Ottawa, ON, Canada, 5Biomedical Engineering, Dalhousie University, Halifax, NS, Canada
Synopsis
Golden-angle sampling allows arbitrary retrospective selection of temporal resolution in dynamic MRI scans. To select the fastest temporal resolution that preserves time course fidelity, we propose the use of image quality metrics (IQMs). We demonstrate multiple IQMs that correlate strongly with the accuracy of fitted pharmacokinetic parameters up to at least an acceleration factor of R=12. For a fixed undersampling factor, these metrics can also inform the selection of reconstruction parameters such as regularization weights for compressed sensing. This approach may enable rational, individual-level tuning of temporal resolution following a prospectively accelerated DCE-MRI scan.
Introduction
Techniques such as GRASP1 and CIRCUS2,3 combine rays or other golden-angle sampling patterns in arbitrary arrangements, allowing the temporal resolution of a dynamic MRI study to be selected retrospectively, and potentially on an individual basis. However, the choice of temporal resolution must be informed by some rational criteria such that the quality of the resulting time series can be assessed and maximized. Recent work4 has indicated that image quality metrics (IQMs) of individual volumes within a simulated DCE-MRI series correlate with the fidelity of the overall time series, as quantified by the accuracy of parameters extracted from model fits. In this work, we present further evidence for correlation between IQMs and pharmacokinetic (PK) parameter map accuracy in clinical DCE-MRI data, which may lead to a framework for automated individual-level selection of temporal resolution to maximize data quality.
Methods
To demonstrate correlations between IQMs and temporal fidelity, retrospective undersampling of clinical DCE-MRI data was performed. Reference data were drawn from DISCO series obtained in N=3 patients indicated for prostate MRI and scanned under an REB-approved protocol. Data were acquired using a GE MR750 3T scanner and an 8-channel receive coil, with 60 phases at 3.8s temporal resolution. Any k-space samples omitted due to parallel imaging or partial-Fourier acquisition were synthesized to create a fully-sampled reference series, which was resampled with quantized CIRCUS3 to achieve acceleration factors of R=2 to 12 (see Figure 1). Different sampling patterns were generated for each time point, with resampled data retaining the temporal resolution of the original series. Undersampled time series were reconstructed using Blind CS5, with 30 dictionary entries, regularization weight λ=0.01, images scaled to a peak intensity of 50. Several alternate reconstruction parameters were evaluated at R=10 to assess discrimination of reconstruction quality at the same undersampling factor.
Each volume of each reconstructed time series was compared to the corresponding volume in the original DISCO series, and the following IQMs were computed: root mean square error (RMSE), structural similarity index (SSIM6) and gradient magnitude similarity deviation (GMSD7). Since the DISCO series were acquired with two echoes for Dixon fat separation, IQMs were computed for the in-phase, out-of-phase, and water images. PK maps were generated using in-house code written in Matlab using an extended Tofts model, with arterial input functions from a manually selected voxel. ROIs were drawn around the prostate, and PK maps from undersampled data were compared with the original DISCO PK maps by computing the normalized RMSE of the Ktrans maps over the ROI. Correlations between IQMs and the RMSE of the Ktrans maps were assessed via the coefficient of determination (R2) of a linear regression model (see e.g. Figure 2).
Results and Discussion
Figure 2 shows correlations between IQMs and PK map fidelity at 7 undersampling factors in each of 3 patients. The IQMs shown are the RMSE and SSIM of the undersampled water image at the first time point, while PK map fidelity is measured by the RMSE of the Ktrans maps. Regression lines at both the individual and group level demonstrate that the precise relationship between IQM and temporal fidelity changes somewhat between patients.
Figure 3 compares several potential IQMs evaluated on (a) the first image of the time series, or (b) all images and averaged over the series. The behavior of the first image is representative of the entire series; IQMs computed for the first volume and for the entire series correlate well with each other, with R2 = 0.91 to 0.99. This is critical since a framework for rapid assessment of reconstruction quality should ideally operate on a subset of the entire series, and proceed to the lengthier time course reconstruction only once a candidate temporal resolution is identified. These results indicate that such an approach is feasible. In both cases the SSIM has the best overall performance, though RMSE and GMSD of the water image also show strong correlation.
Figure 4 demonstrates the evaluation of IQMs over a range of reconstruction choices at the same undersampling factor (R=10). The correlations here are less than with varying undersampling factor, though still strong (R2=0.6 to 0.8 across all varied parameters for most IQMs tested).
Conclusions
The use of image quality metrics represents a potential strategy for evaluating and maximizing the quality of a dynamic MRI time series. Selecting the highest temporal resolution that does not reduce the IQM below a certain threshold, then adjusting reconstruction parameters to further improve quality, may allow near-optimal selection of temporal resolution on an individual basis. This framework is being implemented and assessed for prospectively accelerated DCE-MRI.Acknowledgements
This work is supported by the Atlantic Innovation Fund, an Investigator Sponsored Research Agreement with GE Healthcare, and the NSERC Discovery Grant program (SDB).References
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